Technical Reference | AI Engineering Playbooks

A practical incident runbook for AI agent systems, covering common failure modes and response actions that reduce production impact. Most agent incidents are predictable: tool misuse, context drift, and weak guardrails. Build a failure taxonomy and link each class to detection and recovery playbooks. Track MTTR and recurrence to continuously harden your agent platform. Agent systems do not fail in one way. They fail across planning, context, tool invocation, and execution boundaries. Without a clear runbook, teams lose time arguing about symptoms instead of restoring service. This guide provides an operating model you can implement immediately. Prerequisites Incident severity model (SEV1, SEV2, SEV3). On-call owner for agent platform. Baseline observability for prompts, tool calls, and outcomes. Rollback path for model and policy configuration. Failure taxonomy 1) Intent misclassification The agent chooses the wrong plan for a valid request. Signals: - Wrong w...

A decision framework for when AI agents should act autonomously, when they should seek confirmation, and how to design escalation paths that work under operational pressure. A decision framework for when AI agents should act autonomously, when they should seek confirmation, and how to design escalation paths that work under operational pressure. Your stakeholder alignment and regulated-environment experience makes this a natural and credible topic. Includes controls, pitfalls, and a phased implementation path. A decision framework for when AI agents should act autonomously, when they should seek confirmation, and how to design escalation paths that work under operational pressure. Why this matters Teams are under pressure to deliver AI capability quickly, but speed without control creates operational and governance risk. This guide focuses on practical execution patterns that hold up in production. Prerequisites Clear ownership for delivery and risk decisions. Basel...

How to design multi-agent pipelines with clear orchestration, fallback logic, and accountability — without ending up with a distributed system that no one can debug. How to design multi-agent pipelines with clear orchestration, fallback logic, and accountability — without ending up with a distributed system that no one can debug. Most content on multi-agent systems focuses on possibilities. Includes controls, pitfalls, and a phased implementation path. How to design multi-agent pipelines with clear orchestration, fallback logic, and accountability — without ending up with a distributed system that no one can debug. Why this matters Teams are under pressure to deliver AI capability quickly, but speed without control creates operational and governance risk. This guide focuses on practical execution patterns that hold up in production. Prerequisites Clear ownership for delivery and risk decisions. Baseline observability for model and tool behaviour. Defined quality an...

Side‑by‑side look at emerging agent orchestration frameworks. Side‑by‑side look at emerging agent orchestration frameworks. Offers clarity for technical decision‑making and integration plans. Includes controls, pitfalls, and a phased implementation path. Side‑by‑side look at emerging agent orchestration frameworks. Why this matters Teams are under pressure to deliver AI capability quickly, but speed without control creates operational and governance risk. This guide focuses on practical execution patterns that hold up in production. Prerequisites Clear ownership for delivery and risk decisions. Baseline observability for model and tool behaviour. Defined quality and security acceptance criteria. Practical approach Define the business decision this capability supports. Limit the first release scope to one workflow and one owner. Add measurable controls for quality, latency, and failure handling. Roll out with explicit monitoring and rollback paths. Implemen...

Explore how MCP enables secure, interoperable multi-agent orchestration across tools and vendors, with real-world architecture patterns to move beyond single-agent prototypes. Explore how MCP enables secure, interoperable multi-agent orchestration across tools and vendors, with real-world architecture patterns to move beyond single-agent prototypes. Trending heavily in 2026 reports (Google Cloud, IBM); offers clear benefits in scalable orchestration and vendor neutrality for regulated workloads. Includes controls, pitfalls, and a phased implementation path. Explore how MCP enables secure, interoperable multi-agent orchestration across tools and vendors, with real-world architecture patterns to move beyond single-agent prototypes. Why this matters Teams are under pressure to deliver AI capability quickly, but speed without control creates operational and governance risk. This guide focuses on practical execution patterns that hold up in production. Prerequisites Clear...

A no-fluff comparison of the three dominant agent frameworks — what they're good at, where they break, and how to pick one for production workloads. A no-fluff comparison of the three dominant agent frameworks — what they're good at, where they break, and how to pick one for production workloads. Engineers are picking frameworks based on hype, not fit. Includes controls, pitfalls, and a phased implementation path. A no-fluff comparison of the three dominant agent frameworks — what they're good at, where they break, and how to pick one for production workloads. Why this matters Teams are under pressure to deliver AI capability quickly, but speed without control creates operational and governance risk. This guide focuses on practical execution patterns that hold up in production. Prerequisites Clear ownership for delivery and risk decisions. Baseline observability for model and tool behaviour. Defined quality and security acceptance criteria. Practical ...

How current agent architectures (tool use, multi-step reasoning, memory) are evolving into deployable systems rather than demos. How current agent architectures (tool use, multi-step reasoning, memory) are evolving into deployable systems rather than demos. Agent frameworks like OpenAI’s Evals, CrewAI, and LangGraph are changing the baseline for production AI — engineers need clarity on trade‑offs. Includes controls, pitfalls, and a phased implementation path. How current agent architectures (tool use, multi-step reasoning, memory) are evolving into deployable systems rather than demos. Why this matters Teams are under pressure to deliver AI capability quickly, but speed without control creates operational and governance risk. This guide focuses on practical execution patterns that hold up in production. Prerequisites Clear ownership for delivery and risk decisions. Baseline observability for model and tool behaviour. Defined quality and security acceptance criteri...

A practical list of AI use cases for insurance operations, underwriting support, claims, and service workflows. Insurance teams do not need moonshot AI programmes to create value. They need targeted workflows with clear controls. 10 practical use cases Submission triage and document completeness checks. Broker email summarisation with action extraction. Underwriting note drafting from structured risk inputs. Claims intake classification and routing support. Policy wording comparison assistance. Renewal packet preparation and variance summaries. Internal knowledge retrieval for servicing teams. Meeting preparation briefs for account and placement teams. Escalation risk early-warning summaries. Portfolio-level trend summaries for leadership reviews. Delivery pattern that works Start with one workflow and one business metric. Add human-in-the-loop review for all externally visible outputs. Capture failure cases weekly and retrain prompt/process contracts. Exp...

A practical workflow for using AI-first coding speed while preserving quality, security, and maintainability. Vibe coding is useful for speed, but speed without controls creates technical debt quickly. This workflow keeps velocity while protecting reliability. The 5-step workflow Intent definition : write a one-paragraph spec before prompting. AI generation : generate initial implementation in small modules. Human review : validate architecture, naming, and boundary decisions. Automated checks : lint, tests, type checks, and security scan. Operational check : logging, error paths, and rollback readiness. Non-negotiable guardrails Never merge AI-generated code without human review. Always require tests for changed behaviour. Always check secrets and auth flows manually. Always capture design rationale for non-obvious choices. Where vibe coding works best Prototypes and internal tools. Boilerplate and repetitive integration code. Test scaffolding and docs g...

A practical pre-release checklist for AI features covering security, misuse risk, transparency, and governance. Shipping AI features without security and ethics checks creates hidden operational risk. Use this checklist before each release. 1) Data and privacy Confirm data minimisation in prompts and context. Remove secrets and personal data from logs. Enforce retention windows for model inputs and outputs. Validate third-party processor boundaries. 2) Security controls Restrict tool permissions by role and environment. Validate all tool outputs against strict schemas. Add prompt-injection defences for external content. Require approval gates for high-impact actions. 3) Safety and misuse Define clear disallowed use cases. Add risk prompts for potentially harmful requests. Add user-visible warnings for uncertain outputs. Add abuse monitoring and escalation paths. 4) Transparency and trust Disclose where AI assistance is used. Explain known limitations...

A practical architecture for building AI agents with MCP, including boundaries, observability, and failure handling. AI agents are moving from demos to production systems, and MCP is quickly becoming a common protocol for tool and context integration. This guide covers a practical baseline architecture. Why this matters now As of 2025-2026, MCP support and agent workflows have expanded across major ecosystems, and teams need interoperable patterns rather than provider lock-in. Baseline architecture Orchestrator layer : plans tasks, manages tool calls, and handles retries. Model layer : reasoning/generation model with explicit prompt contracts. MCP tool layer : context servers for docs, repos, tickets, and internal systems. Policy layer : security rules, redaction, and allowed-tool boundaries. Observability layer : traces, token costs, tool latency, failure telemetry. Key design rules Treat MCP servers as untrusted inputs unless explicitly verified. Whitelist to...

This site is now focused on practical AI engineering: agents, MCP, frameworks, security, ethics, and product delivery. If you are building with AI and need practical, implementation-first guidance, this site is for you. Technical Reference is now focused on modern AI engineering with one rule: Useful in production beats impressive in demos. What you will find here AI agent architecture and MCP integration guides. Framework comparisons that include trade-offs, not marketing claims. Security and ethics checklists for real delivery contexts. Vibe coding workflows with reliability guardrails. AI + insurance applied patterns. Idea-to-product execution playbooks. How to use this site Start with implementation guides. Use comparison posts to choose a stack. Apply checklists before release. Revisit trend posts as the ecosystem changes. Publishing rhythm Tuesday: technical implementation guide. Friday: strategic comparison, trend, or case-based post. What to re...